Early detection of hip periprosthetic joint infections through CNN on Computed Tomography images

• DOI: 10.48550/arxiv.2304.08942
• Publication Date: 2023-01-01
• Authors: Francesco Guarnera; Alessia Rondinella; Oliver Giudice; Alessandro Ortis; Sebastiano Battiato; Francesco Rundo; Giorgio Fallica; Francesco Traina; Sabrina Conoci
• Keywords: Image and Video Processing (eess.IV); FOS: Electrical engineering, electronic engineering, information engineering; Electrical Engineering and Systems Science - Image and Video Processing; 3. Good health

Early detection of an infection prior to prosthesis removal (e.g., hips, knees or other areas) would provide significant benefits to patients. Currently, the detection task is carried out only retrospectively with a limited number of methods relying on biometric or other medical data. The automatic detection of a periprosthetic joint infection from tomography imaging is a task never addressed before. This study introduces a novel method for early detection of the hip prosthesis infections analyzing Computed Tomography images. The proposed solution is based on a novel ResNeSt Convolutional Neural Network architecture trained on samples from more than 100 patients. The solution showed exceptional performance in detecting infections with an experimental high level of accuracy and F-score.